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Skills/launchdarkly/agent-skills/launchdarkly-metric-instrument
launchdarkly-metric-instrument logo

launchdarkly-metric-instrument

launchdarkly/agent-skills
2K installs17 stars
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Installation

npx skills add https://github.com/launchdarkly/agent-skills --skill launchdarkly-metric-instrument

Summary

Instrument a LaunchDarkly metric event in a codebase by adding a track() call. Use when the user wants to wire up an event, instrument an action for a metric, add tracking to a feature, or confirm that an event is flowing to LaunchDarkly.

SKILL.md

LaunchDarkly Metric Instrument

You're using a skill that will guide you through adding a track() call to a codebase so a LaunchDarkly metric can measure it. Your job is to detect the SDK in use, find the right place in code to add the call, write it correctly, and verify that events are reaching LaunchDarkly.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Required MCP tools:

  • list-metric-events — verify events are flowing after instrumentation

Optional MCP tools (enhance workflow):

  • get-project — retrieve the SDK key for the right environment when SDK initialization is needed

Workflow

Step 1: Detect the SDK

Before writing any code, understand the LaunchDarkly setup already in this codebase.

  1. Search for existing track() calls. This is the fastest signal:
  • Look for ldClient.track(, .track(, ld.track(
  • If any exist, they tell you the SDK type, call signature, and context pattern in one shot — mirror those exactly.
  1. Search for SDK imports and initialization if no track() calls exist:
  • Check package.json, requirements.txt, go.mod, Gemfile, *.csproj for an LD SDK dependency
  • Look for LDClient, ldclient, launchdarkly-server-sdk, launchdarkly-node-server-sdk, launchdarkly-react-client-sdk, etc.
  • Find the initialization block to understand how the client is accessed across the codebase
  1. Determine client-side or server-side. This is the most critical distinction — it determines the track() signature:
SDK typetrack() signatureNotes
Server-side (Node, Python, Go, Java, Ruby, .NET)ldClient.track(eventKey, context, data?, metricValue?)Context required per call
Client-side (React, browser JS)ldClient.track(eventKey, data?, metricValue?)Context set at init, not per call

See SDK Track Patterns for full examples by language.

Step 2: Install & Initialize (if SDK not present)

Skip this step if the SDK is already in the codebase.

  1. Detect the package manager from lockfiles: package-lock.json / yarn.lock / pnpm-lock.yaml → npm/yarn/pnpm; Pipfile.lock / poetry.lock → pip/poetry; go.sum → go modules; Gemfile.lock → bundler.
  1. Install the appropriate SDK using the detected package manager. See SDK Track Patterns for the right package name per language.
  1. Get the SDK key using get-project — fetch the project and choose the key for the environment the user wants to instrument (typically production or staging for initial testing).
  1. Add SDK initialization following the patterns already in this codebase. If there's a central config or service layer, add the LD client there. See SDK Track Patterns for initialization examples.

Step 3: Find the Right Placement

Locate where in the code the user action or event occurs.

  1. Ask if you're not sure where the action happens. Don't guess at placement — a track() call in the wrong location (e.g. a render method instead of a submit handler) produces misleading data.
  1. Look for signals of the right location:
  • Form submissions, button click handlers, API route completions, mutation hooks
  • Existing analytics calls (segment.track(), mixpanel.track(), gtag()) — these are often co-located with where LD track calls should go
  • Comments like // TODO: track this
  1. Show the candidate location to the user before writing anything:
   I'll add the track() call here, in the checkout submit handler (src/checkout/CheckoutForm.tsx, line 47).
   Does that look right?
  1. Proceed once confirmed (or if you're confident enough from codebase signals).

Step 4: Write the track() Call

Write the call following the patterns found in Step 1.

Server-side SDKs — context is required:

ldClient.track('checkout-completed', context);

Client-side SDKs — context is implicit:

ldClient.track('checkout-completed');

For value metrics — include metricValue with the numeric measurement:

// Server-side: latency metric (ms)
ldClient.track('api-response-time', context, null, responseTimeMs);

// Client-side: revenue metric
ldClient.track('purchase-completed', { orderId }, purchaseAmountUSD);

Key rules:

  • Match the existing context. Don't construct a new context inline. Find where the codebase already builds its context/user object (used for variation() calls) and use the same one. This is how LD correlates the event to the right experiment participant.
  • metricValue only for value metrics. For count and occurrence metrics, omit metricValue entirely.
  • Respect wrapper patterns. If the codebase wraps LD calls behind a utility (featureFlags.track(), analytics.ldTrack()), add the new call through that wrapper — not by calling ldClient directly.
  • Match the event key exactly. track() event keys are case-sensitive. Use the exact string that the metric was created with.

See SDK Track Patterns for full per-language examples.

Step 5: Verify

Guide the user to trigger the action in their local or staging environment. Then use list-metric-events to confirm the event key appears:

list-metric-events(projectKey, environmentKey)

If the event key appears: confirm success and show a summary.

If the event key is absent after triggering, work through this checklist:

ProblemCheck
Wrong event key casingDoes the track() call match the metric's event key exactly?
SDK not initializedIs ldClient initialized before the track() call runs?
Server-side: wrong contextIs the context passed to track() the same context used for variation() calls?
Client-side: no flag evaluation firstHas the SDK initialized and identified the user before track() is called?
Wrong environmentIs list-metric-events querying the same environment where the action was triggered?
Data delaylist-metric-events shows the last 90 days with up to ~5 min delay — try again in a moment

Surface a summary once verified:

✓ Event flowing: checkout-completed
  Seen in: production
  
Next: this event is now ready to back a metric. Use the metric-create skill to set one up,
or attach an existing metric to your experiment.

Important Context

  • track() calls only count in experiments when a flag is evaluated first. The event is correlated to an experiment participant because LD saw a variation() call from that context. If the user triggers the action without evaluating any flag, the event may still be ingested but won't appear in experiment results.
  • Client-side SDKs flush events on an interval (default ~30 seconds) or on page unload. In tests, you may need to call ldClient.flush() explicitly to see events appear immediately.
  • Server-side SDKs also buffer events. Calling ldClient.flush() after track() in development ensures the event is sent before the process exits or the test ends.
  • metricValue units must match the metric definition. If the metric was created with unit ms, pass milliseconds. Passing seconds into a milliseconds metric will produce silently wrong results.
  • The data parameter is for custom metadata, not the metric value. Pass extra context (order ID, category, etc.) in data. Pass the numeric measurement in metricValue.

References

  • SDK Track Patterns — track() call syntax, initialization, and package names for every supported SDK

Score

0–100
63/ 100

Grade

C

Popularity15/30

1,618 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

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Markdown

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HTML

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Launchdarkly Metric Instrument FAQ

How do I install the Launchdarkly Metric Instrument skill?

Run “npx skills add https://github.com/launchdarkly/agent-skills --skill launchdarkly-metric-instrument” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Launchdarkly Metric Instrument skill do?

Instrument a LaunchDarkly metric event in a codebase by adding a track() call. Use when the user wants to wire up an event, instrument an action for a metric, add tracking to a feature, or confirm that an event is flowing to LaunchDarkly. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Launchdarkly Metric Instrument skill free?

Yes. Launchdarkly Metric Instrument is a free, open-source skill published from launchdarkly/agent-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Launchdarkly Metric Instrument work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Launchdarkly Metric Instrument works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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